检测并缓解招聘中预训练嵌入的性别偏见，提升AI筛选公平性。
Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment
- 用对抗学习抑制嵌入中隐含的性别信息。
- 去性别化文本仍存偏见，对抗训练在原始文本上效果更佳。
- 多目标优化选择兼顾准确与公平的模型，适合招聘系统开发者。
基于机器学习的招聘系统依赖历史简历数据训练，可能延续甚至放大社会偏见。关键挑战在于非结构化简历文本中，即使显式性别信息被移除，预训练语言模型嵌入仍可能推断出敏感属性如性别。本文在合成数据集FairCVdb上评估了九种预训练嵌入模型，分析其在原始及去性别化简历上的可预测性与性别泄露风险。进一步采用带有梯度反转的多任务对抗学习框架，在预测候选人适配度的同时抑制嵌入中的性别信息。最后通过多目标帕累托前沿模型选择，平衡预测性能与公平性。实验表明，显式去性别化可显著降低但无法完全消除性别泄露；对抗学习主要在原始简历上提升公平性，作为文本级去偏的补充策略而非替代方案。
原文摘要 · Abstract (English)
AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases. A key challenge arises in unstructured CV text, where pre-trained language model embeddings may infer sensitive attributes such as gender even after explicit indicators are removed. In this paper, we evaluate nine pre-trained embedding models on the synthetic FairCVdb dataset, analyzing the informativeness of their embeddings for applicant scoring and their susceptibility to gender leakage, on both original and gender-scrubbed biographies. We further use a multi-task adversarial learning framework with gradient reversal to predict applicant suitability while suppressing gender information from learned representations. Finally, we use a multi-objective Pareto-front-based model selection to balance predictive utility and fairness. Our experimental results show that explicit gender scrubbing substantially reduces but does not eliminate gender leakage, while adversarial learning improves fairness mainly on original biographies and acts as a complementary strategy rather than a substitute for text-level debiasing.
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